Instantaneous Energy Consumption Research Articles

Powertrain Design and Modeling for a Fuel Cell Hybrid ElectricVehicle

The objective of this study was to develop a Fuel Cell Hybrid Electric Vehicle (FCHEV) powertrain with the aim of enhancing battery usage autonomy. The vehicle, which participated in the Eco-Marathon competition as a prototype, incorporates batteries, a Direct Current (DC) electric motor, and a Proton Exchange Membrane (PEM) fuel cell. The design permits the operation of the fuel cell to be conducted in a more efficacious and fuel-efficient manner. The study employs the MATLAB-Advisor software to construct powertrain models that are then validated in laboratory settings. These models are subsequently compared with the performance of the actual FCHEV prototype and adapted for use in automotive applications. The FCHEV power model calculates instantaneous energy consumption using input variables, such as vehicle speed, acceleration, and road gradient. Furthermore, Real Cycle drive was carried out to improve the trade-off between energy consumption, fuel cells, battery State of Charge (SOC) dynamics, and battery power smoothness, while ensuring that all essential limitations were met. The addition of a fuel cell to an electric car model enhances its range by 250%, significantly improving its adoption and usage.

Methodology for developing models to estimate vehicle instantaneous energy consumption based on hub-type dyno test data

This paper describes a methodology to develop simple energy consumption models of road vehicles exploiting transient experimental datasets obtained from a vehicle/powertrain four-dyno testbed available at the Center for Automotive Research and Sustainable mobility (CARS@POLITO) of Politecnico di Torino. These models, based on a locally weighted linear regression method, can serve as a simpler alternative to more conventional methods based, for example, on engine maps obtained by steady-state characterization at engine testbeds, and combined with powertrain subsystem models. The present methodology was applied to a conventional diesel-powered vehicle. Three different modeling approaches are proposed: vehicle-based (VB), engine-based (EB) and engine-based modified (EB*). The VB approach is the simplest, being able to estimate the vehicle fuel consumption by only using, as inputs, wheel torque and speed, while the EB and EB* approaches enhance modeling accuracy by using engine speed and torque, as inputs, along with transmission-related parameters and/or by considering the moments of inertia of the powertrain rotating parts. The manuscript describes, in full, the process used to develop these models, providing significant guidance for researchers who may want to replicate the procedure with their own experimental data. These energy consumption models can be useful tools for the development and assessment of eco-driving or ADAS functions or for energy consumption comparison between different vehicles that were not tested on the same driving cycle. They can also support the estimation of the total energy consumption of vehicles along different traffic conditions or routes, based on a limited number of experiments and low computational effort.

High-current decoupled hydrogen and oxygen evolution via nickel–cobalt based redox mediators and bifunctional catalyst of 3D printing substrates

The conversion of renewable energy sources with relatively large energy fluctuations into hydrogen represents a crucial aspect of energy storage. Nevertheless, the direct water electrolysis process is known to require excessive instantaneous energy consumption and high cost. Two-step alkaline water electrolysis is regarded as a secure and effective method of generating hydrogen from renewable energy sources when compared to direct water electrolysis. Here we propose a two-step alkaline water electrolysis using nickel–cobalt based hydroxide (Ni0.9Co0.1(OH)2) as a redox mediator, and a high-performance bifunctional catalyst as gas evolution electrodes (GEE). The substrates for the GEE were prepared using 3D printing and then loaded with in-situ grown Ru-doped MoS2/NiFe-LDH hierarchical heterostructure catalysts (MS-NiFe-Ru-3D). The oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) of the MS-NiFe-Ru-3D catalyst can reach up to 500 mA cm−2 at 300 and 250 mV overpotentials, respectively. It can meet the requirement of high catalyst performance for two-step alkaline water electrolysis. The direct water electrolysis using the bifunctional MS-NiFe-Ru-3D catalyst only requires a voltage of 1.85 V at 500 mA cm−2 with minimal attenuation over 300 h. For the two-step alkaline water electrolysis using MS-NiFe-Ru-3D as bifunctional catalysts and Ni0.9Co0.1(OH)2 as redox mediator, only 1.70 V and 0.27 V were required for HER and OER at 500 mA cm−2, respectively. This work offers a promising avenue for the efficient conversion of renewable secondary energy sources into hydrogen.

Research on Optimal Driving Torque Control Strategy for Multi-Axle Distributed Electric Drive Heavy-Duty Vehicles

Multi-axle distributed electric drive heavy-duty vehicles have the characteristics of high transmission efficiency, strong maneuverability, and good passability, making them widely used in large cargo transportation. However, the current driving torque control strategies of multi-axle distributed electric drive heavy-duty vehicles lack comprehensive consideration of their longitudinal and lateral dynamic characteristics, making it difficult to comprehensively optimize multiple performances such as power economy, comfort, and stability. In order to solve the above problems, This work focuses on a five-axle distributed electric drive heavy-duty vehicle. Firstly, given the differences in dynamics between two-axle vehicles and multi-axle vehicles, the dynamic model of the multi-axle distributed electric drive heavy-duty vehicle and its critical components is constructed. Then, by analyzing the characteristics of power economy, comfort, and stability of the multi-axle distributed electric drive heavy-duty vehicle, an optimal driving torque control strategy based on multiple performance coordination is proposed. Finally, on the hardware-in-the-loop (HiL) platform, the performance of the optimal driving torque control strategy proposed in this paper is verified by using the China Heavy-Duty Commercial Vehicle Test Cycle for Truck (CHTC-HT) and a straight-line acceleration driving condition on a split friction road. The simulation test results show that, compared with the traditional torque average distribution strategy, the proposed optimal driving torque control strategy can reduce the energy consumption rate by 3.45% in CHTC-HT. The strategy is attributed to the driving torque distribution based on the vehicle’s optimal instantaneous energy consumption, and vehicle comfort is also ensured by the driving mode switching frequency suppression. Subsequently, the vehicle’s stability on the split friction road is effectively improved by the torque coordination compensation strategy. This control strategy significantly improves the comprehensive performance of multi-axle distributed electric drive heavy-duty vehicles.

Sensor-based modelling of suction sails to integrate into a numerical simulation tool for a wind-assisted vessel and its application to green shipping

In order to achieve reductions in ship emissions and operational costs while complying with increasingly stringent regulations, ship owners need to consider combining multiple efficiency enhancing strategies where synergistic benefits are possible. One such option is to couple wind assistance with speed reduction. Realizing this solution’s maximum potential requires both an experimental characterization of the forces present when the vessel is sailing as well as a numerical tool to analyse instantaneous energy consumption at specific conditions to suggest optimal operating strategies over a route. Therefore, this paper focuses on processing strain gauge measurements for real-world wind propulsion units, coupled with a numerical modelling tool for a wind-assisted container vessel. These measurements are then integrated into the numerical model and simulations are conducted to suggest improved operating strategies for increased fuel efficiency of a vessel across different speeds and along a route while considering real-world wind conditions. The results suggest that an increased travel time through speed reduction of approximately 8% can result in a fuel consumption decrease of nearly 16% due to the reduction in ship resistance and the increasing share of propulsive thrust provided by wind-assistance.

Non-dominated sorting artificial rabbit multi-objective sizing optimization for a conceptual powertrain of a 6 × 4 battery electric tractor truck

In order to tackle the increasingly severe energy crisis, improving the efficiency of the powertrain system in battery electric vehicles has become an effective approach. Based on a 6 × 4 battery electric tractor truck (BETT), the paper focuses on enhancing the dynamic and economic performance of vehicles through advancements in configuration, control strategies, and parameter design. Firstly, a novel multi-motor torque coupling powertrain system based on the parallel-axle transmission is proposed and modeled. Then, an instantaneous minimum energy consumption control strategy for the economic performance simulation and an instantaneous maximum acceleration control strategy for the dynamic performance simulation are designed. Furthermore, a conventional heuristic single-objective optimization algorithm, artificial rabbit optimization (ARO), is improved into a multi-objectives version, non-dominated sorting artificial rabbit multi-objective optimization (NSARMO), by combining the fast non-dominated sorting (FNS) and modifying the calculation method of the energy factor. Finally, an optimization framework consisting of a control layer, model layer, and optimization layer is established and applied to the parameter optimization of the proposed powertrain system. Simulation results demonstrate that the optimization results of the proposed method are superior to those optimized by commonly used non-dominated sorting genetic algorithm-II (NSGA-II) and NSARMO without the modified energy factor, achieving a better balance between dynamic performance and economic performance. Moreover, the proposed method exhibits demonstrates 40.67% and 24.67% improvement in its ability to search for optimal Pareto solutions compared to NSARMO (-)(- means the calculation method of energy factor is not modified) and NSGA-II. At last, a hardware-in-the-loop (HIL) experiment bench is established, and the experiment results indicate that a scheme optimized by the proposed NSARMO exhibits only an approximate performance in energy consumption in the HIL environment compared to the simulation environment, which signifies the effectiveness of control strategy equipped with the optimization results optimized by the proposed method.

Validation of a statistical-dynamic framework for predicting energy consumption: A study on vehicle energy conservation equation

Predictive models for vehicle energy consumption are crucial for sustainable development in urban road traffic systems. This paper comprehensively reviews classic predictive models and develops a novel statistical-dynamical energy consumption prediction framework called Vehicle Energy Conservation Equation (VECE). VECE is constructed based on the principles of vehicle energy flow and regression analysis, employing a continuous and concise mathematical formulation. Its coefficients possess clear physical interpretations, allowing for application in various vehicle categories. To validate VECE, this study collected energy consumption data from 28 vehicles, including 9 diesel vehicles, 16 gasoline vehicles, 1 ethanol gasoline vehicle, and 2 battery electric vehicles. A rigorous data processing procedure was designed. Data analysis revealed that the VECE coefficients are correlated with vehicle type, speed, and acceleration. VECE's predictive performance is minimally impacted by the number of vehicle categories, effectively modeling energy consumption for vehicles of the same fuel type or size category. Comparative analysis with E-EcoGest, VT-Micro, PERE, and CMEM demonstrates the moderate accuracy of VECE in predicting instantaneous vehicle energy consumption while excelling in predicting cumulative energy consumption. The minimum relative percentage error for the cumulative predicted values is 4.2%. Overall, VECE demonstrates outstanding performance in computational simplicity, coefficient interpretability, adaptability, and extensibility, making it a crucial tool for achieving energy-efficient road transport systems.

Identification method for unsteady friction of tool flank for high efficiency milling cutter

Under the cutting loads of high efficiency and intermittent, the friction status between the tool flank and transition surface has time-varying characteristics, which directly affects friction and wear performance of the milling cutter. The dynamic distribution of instantaneous friction on tool flank under milling vibration needs to be revealed. In this work, the effect of milling vibration on instantaneous cutting contact relationship between milling cutter and workpiece was researched, a calculation method for the instantaneous friction velocity and friction energy consumption on tool flank was proposed. The variation of instantaneous friction between tool flank and transition surface under milling vibration was identified and evaluated by power spectral entropy and cross-correlation function. A method for identifying the unsteady friction of tool flank was proposed. The response analysis and experimental verification were conducted. The results showed that the frequency domain parameter distribution of friction energy consumption on tool flank had a strong correlation with the wear depth distribution, and was obviously sensitive to the milling parameters. The above models and methods could be used to reveal the influence of key milling process variables on unsteady friction and wear process of milling cutter’s multiple tool flank.

Multi-Feature Fusion-Based Instantaneous Energy Consumption Estimation for Electric Buses

Multi-Feature Fusion-Based Instantaneous Energy Consumption Estimation for Electric Buses

A problem-specific knowledge based artificial bee colony algorithm for scheduling distributed permutation flowshop problems with peak power consumption

A distributed permutation flowshop scheduling problem (DPFSP) with peak power consumption is addressed in this work. The instantaneous energy consumption of each factory cannot exceed a threshold. First, a mathematical model is developed to describe the concerned problem. Second, an improved artificial bee colony (IABC) algorithm is proposed. Based on problem-specific knowledge, three new solution generation operators, e.g., shift, swap, and speed adjust, are designed for employ bees and onlooker bees. A local search operation is developed to improve the quality of current best-known solution in each iteration. 450 instances are solved to evaluate the performance of IABC via comparing to seven state-of-the-art algorithms. The average relative percentage increase (ARPI) of IABC ranks 1 among all compared algorithms. The results and discussions show that the proposed IABC algorithm has strong competitiveness for solving the DPFSP with peak power consumption.

Simple Diesel Train Fuel Consumption Model for Real-Time Train Applications

This paper introduces a simple diesel train energy consumption model that calculates the instantaneous energy consumption using vehicle operational input variables, including the instantaneous speed, acceleration, and roadway grade, which can be easily obtained from global positioning system (GPS) loggers. The model was tested against real-world data and produced an error of −1.33% for all data and errors ranging from −12.4% to +8.0% for energy consumption of four train datasets amounting to a total of 5854 km trips. The study also validated the proposed model with separate data that were collected between Valencia and Cuenca, Spain, which had a total length of 198 km and found that the model was accurate, yielding a relative error of −1.55% for the total energy consumption. These results show that the proposed model can be used by train operators, transportation planners, policy makers, and environmental engineers to evaluate the energy consumption effects of train operational projects and train simulation within intermodal transportation planning tools.

Adaptive Instantaneous Equivalent Energy Consumption Optimization of Jydrogen Fuel Cell Hybrid Electric Tram

Adaptive Instantaneous Equivalent Energy Consumption Optimization of Jydrogen Fuel Cell Hybrid Electric Tram

Characterization of flow field of swirling flow conveying in bulk grain pipeline based on gas‐solid multiphase coupling

AbstractAn experiment was carried out to study the characteristics of swirling pneumatic conveying of grain particles in horizontal pipeline, which can provide a reference for the design and optimization of swirling pneumatic conveying system of bulk grain pipeline. The numerical simulation of swirling pneumatic conveying under the guidance of vane swirling generator was performed via CFD‐DEM coupling method. The axial velocity of grain particles was symmetrical along the radial direction, and the tangential velocity was symmetrical along the pipe axis. When the blade number of the swirling device was 6–9, the static pressure drop and standard deviation of the flow field were large, the instantaneous energy consumption of the system and the dispersion of particles were high, and the conveying effect was good. When the blade number was six, the spiral distance of the flow field in the pipe was longer with better quality of swirl flow. In accordance with the simulation results, the best effect of swirling conveying is achieved when the equivalent diameter of the grain particles is 4 mm, the multiphase flow conveying speed is 20 m/s, and the spiral blade number of the swirling flow generator with flow deflector is six.Practical applicationIn the practical application, the swirling flow generated by the swirling device can suppress the settlement of particles and the blockage of the pipeline in the straight pipe conveying, and avoid the high speed collision between particles and the pipe wall in the curved pipe conveying, slow down the impact, reduce the particle breakage and the wear of the pipe wall. The generation and maintenance of swirling flow play a key role in the long‐distance conveying of various chemical raw materials, grain, fertilizer and other granular materials. Swirling flow pneumatic conveying has the advantages of simple equipment structure, small occupation space, flexible pipeline layout, easy to automatic control, high production efficiency, recycling, environmental protection, and so on. It has become an important conveying mode in bulk material conveying.

Research on Multi-Mode Drive Optimization Control Strategy of Four-Wheel-Drive Electric Vehicles with Multiple Motors

Four-wheel-drive electric vehicles with multiple motors have several advantages such as energy saving, environmental protection, and excellent dynamics performance. Therefore, they have become an important development trend in the field of electric vehicles in the past few years. To deal with the problems of the economy and dynamics optimization of an electric vehicle with dual motors on the front axle and a single motor on the rear axle, a multi-mode drive optimization control strategy based on the hierarchical control architecture is proposed in this paper. In the strategy, the reference torque distribution strategy of the front and rear motors is obtained by optimizing the instantaneous energy consumption power in the economy module, and a torque compensation strategy is formulated based on the fuzzy control in the dynamics module to improve the vehicle dynamics in high dynamics demand conditions. Finally, the closed-loop cycle test and the driver-in-the-loop test are carried out to verify the performance of the strategy proposed in this paper, and the results show that energy consumption is reduced by 6.45% in the World Harmonized Light-duty Vehicles Test Cycle (WLTC), and that vehicles accelerate more quickly in variable acceleration conditions under the proposed strategy.

Environment Classification Using Machine Learning Methods for Eco-Driving Strategies in Intelligent Vehicles

This work presents the development of a classification method that can contribute to precise and increased awareness of the situational context of vehicles, for it to be used in autonomous driving applications. This work aims to obtain a method for machine-learning-based driving environment classification that does not involve computer vision but instead makes use of dynamics variables from Inertial-Measurement-Unit (IMU) sensors and instantaneous energy consumption measurements. This article includes details about the data acquisition, the electric vehicle used for the experiments, and the pre-processing methods employed. This explores the viability of a method for classifying a vehicle’s driving environment. The results of such a system can potentially be used to provide precise information for path planning, energy optimization, or safety purposes. Information about the driving context could be also used to decide if the conditions are safe for autonomous driving or if human intervention is recommended or required. In this work, the feature selection process and statistical data pre-processing methods are evaluated. The pre-processed data are used to compare 13 different classification algorithms and then the best three are selected for further testing and data dimensionality reduction. Two approaches for feature selection based on feature importance and final classification scores are tested, achieving a classification mean accuracy of 93 percent with a real testing dataset that included three driving scenarios and eight different drivers. The obtained results and high classification accuracy represent a first approach for the further development of such classification systems and the potential for direct implementation into autonomous driving technology.

Instantaneous friction energy consumption and its evolution of high energy efficiency milling cutter

In the process of high-efficiency milling of titanium alloys, cutting vibration causes the frequent changes in the contact relationship between the flank face of the cutter tooth and the workpiece. The instantaneous friction speed, friction force and energy consumption of the milling cutter thus change continuously, resulting in the difficulty to precisely control the wear and life of milling cutter. This has become a bottleneck of restricting the further improvement of the cutting performance of high energy efficiency milling cutter. In this work, the model of instantaneous friction velocity and friction energy consumption of the flank face of the cutter tooth under vibration are developed. The distribution and evolution of instantaneous friction speed and friction energy consumption are studied. The proposed models are verified by high-efficiency milling experiments. The results show that the average relative error of the proposed model is 10.05%, by using this model, the influences of the cutting vibration on instantaneous friction energy consumption and friction wear boundaries could be effectively unveiled. The proposed model also could accurately describe the distribution and evolution of the above-mentioned parameters.

Method of Reduction in Energy Consumption by the Drive Systems of a Mobile Device with a Controlled Gear Ratio

The degree of autonomy of a battery-powered mobile device depends, among others, on the efficient use of energy by the powered devices. Using an example of electric drive systems of an orthotic robot, the authors present a method of reducing the energy demand of these systems by using a gear with a controlled ratio. The gear ratios are selected on the basis of special graphs illustrating the instantaneous energy consumption during drive operations. The simulation studies proved a possibility of achieving energy savings during the implementation of the robotic functions of the robot as high as 50%. The article presents the course and results of the research as well as the concept of their use while designing electric drive systems for mobile devices.

Developing a Hydrogen Fuel Cell Vehicle (HFCV) Energy Consumption Model for Transportation Applications

This paper presents a simple hydrogen fuel cell vehicle (HFCV) energy consumption model. Simple fuel/energy consumption models have been developed and employed to estimate the energy and environmental impacts of various transportation projects for internal combustion engine vehicles (ICEVs), battery electric vehicles (BEVs), and hybrid electric vehicles (HEVs). However, there are few published results on HFCV energy models that can be simply implemented in transportation applications. The proposed HFCV energy model computes instantaneous energy consumption utilizing instantaneous vehicle speed, acceleration, and roadway grade as input variables. The mode accurately estimates energy consumption, generating errors of 0.86% and 2.17% relative to laboratory data for the fuel cell estimation and the total energy estimation, respectively. Furthermore, this work validated the proposed model against independent data and found that the new model accurately estimated the energy consumption, producing an error of 1.9% and 1.0% relative to empirical data for the fuel cell and the total energy estimation, respectively. The results demonstrate that transportation engineers, policy makers, automakers, and environmental engineers can use the proposed model to evaluate the energy consumption effects of transportation projects and connected and automated vehicle (CAV) transportation applications within microscopic traffic simulation models.

Real-time dynamic predictive cruise control for enhancing eco-driving of electric vehicles, considering traffic constraints and signal phase and timing (SPaT) information, using artificial-neural-network-based energy consumption model

This paper proposes a real-time dynamic predictive cruise control (PCC) system to minimize the energy consumption for electric vehicles (EVs) under integrated traffic situations with synthetic driving scenarios, considering both constraints from the preceding vehicle and the influence of traffic signal lights. The proposed PCC system is working based on the bi-level model predictive control (MPC) algorithm. The Signal Phase and Timing (SPaT)-oriented MPC calculates a desired acceleration command as the optimal control signal at each sampling step based on the forthcoming SPaT information with the purpose of passing the nearest signalized intersection during the green light interval without stop. The car-following-oriented MPC executes preceding vehicle tracking task through maintaining a safe inter-distance using a customized variable time headway (VTH) strategy. The instantaneous energy consumption for EV in different traffic scenarios was quantified by a data-driven model. The developed system was validated through comparison with IDM and human driver's maneuver in both suburban and urban areas road in the city of Fukuoka, Japan, during off-peak and peak hours, using the real traffic system and SPaT data. To further evaluate the performance of the proposed PCC system in high speed driving situation, another case study with transitions from highway to urban road was conducted. The simulative results showed that the proposed PCC system can realize the energy-saving rates by 8.5%–15.6%. And it was working well and robustly under high speed driving situation.

Minimization of power and energy consumption of mechatronic drives in robotics and technological equipment

The maximum instantaneous power consumption of robot drives determines the requirements for the energy supply system and the dimensions of the machine. For numerous machines, there are no technological restrictions on the types of applied motion laws and their numerical characteristics i.e. maximum speeds and accelerations. The type of the motion law and especially its parameters are traditionally determined according to the preferences of the design engineer without any justification, though some-times restrictions on maximum accelerations or speeds are considered. The restrictions on maximum accelerations are related to ensuring the strength and accuracy of the drive, and the restrictions on maximum speeds are related to the safety of personnel in the workplace. The motor power is selected according to the maximum value of the instantaneous power and thermal load, which depends on the duration of switching on. The article analyzes the ways of minimizing of this maximum (peak) of instantaneous power inside the cycle for different laws and different loads. The main parameter by which the maximum (peak) power is minimized for all types of laws is the acceleration and braking times. On the example of the most common motion laws, the dependence of instantaneous power and energy consumption on accelerating time and braking time for various types of loads are studied. In this article, the dependence of instantaneous power and energy consumption on accelerating time and braking time for various types of loads are studied on the example of the most common motion laws. The research results are intended to create a design technique for drives of modern equipment.